Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry

de Carvalho, Isabella Marques; da Silva Mutz, Yhan; Machado, Amanda Cristina Gomes; de Lima Santos, Amanda Aparecida; Magalhães, Elisângela Jaqueline; Nunes, Cleiton Antônio

doi:10.3390/foods12183436

Cited by 2 publications

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References 35 publications

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“…However, they highlighted a possible drawback of inhomogeneous sample images, which might have contributed to the unsatisfactory classification performance for the oil blends using the colour sensor under UV light. The evident difference in performance attributed to different light sources aligns with the known significance of illumination in colourimetric techniques [26][27][28].…”

Section: Discussionmentioning

confidence: 68%

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil

Lorenzo,

da Rocha,

Papaioannou

et al. 2024

Foods

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This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17–3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

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Section: Discussionmentioning

confidence: 68%